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    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Visualizing Data: plotly express</strong></font>

    Visualizing Data: plotly express

    xxxxxxxxxx
    There are many ways to interact with data, and one of the most powerful modes of interaction is through **visualizations**. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: [pandas](../%40textbook/07-visualization-pandas.ipynb), [Matplotlib](../%40textbook/06-visualization-matplotlib.ipynb), plotly express, and [seaborn](../%40textbook/09-visualization-seaborn.ipynb). In this section, we'll focus on using plotly express.

    There are many ways to interact with data, and one of the most powerful modes of interaction is through visualizations. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: pandas, Matplotlib, plotly express, and seaborn. In this section, we'll focus on using plotly express.

    xxxxxxxxxx
    # Scatter Plots

    Scatter Plots¶

    xxxxxxxxxx
    A **scatter plot** is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for **correlations**.

    A scatter plot is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for correlations.

    [1]:
     
    import pandas as pd
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1.head()
    [1]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    After cleaning the data, we can use plotly express to draw scatter plots by specifying the DataFrame and the interested column names.

    After cleaning the data, we can use plotly express to draw scatter plots by specifying the DataFrame and the interested column names.

    [2]:
     
    import plotly.express as px
    ​
    fig = px.scatter(mexico_city1, x="price", y="surface_covered_in_m2")
    fig.show()
    020M40M60M80M100M120M02000400060008000
    pricesurface_covered_in_m2
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Plot the scatter plot for column "price" and "surface_total_in_m2".

    [3]:
     
    fig = px.scatter(mexico_city1,x="price",y="surface_covered_in_m2")
    fig.show()
    020M40M60M80M100M120M02000400060008000
    pricesurface_covered_in_m2
    plotly-logomark
    xxxxxxxxxx
    # 3D Scatter Plots

    3D Scatter Plots¶

    Scatter plots can summarize information in a DataFrame. Three dimensional scatter plots look great, but be careful: it can be difficult for people who might not be sure what they're looking at to accurately determine values of points in the plot. Still, scatter plots are useful for displaying relationships between three quantities that would be more difficult to observe in a two dimensional plot.

    Let's take a look at the first 50 rows of the mexico-city-real-estate-1.csv dataset.

    [4]:
     
    import pandas as pd
    import plotly.express as px
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1_subset = mexico_city1.loc[1:50]
    ​
    fig = px.scatter_3d(
        mexico_city1_subset,
        x="Borough",
        y="surface_covered_in_m2",
        z="price",
        symbol="property_type",
        color="property_type",
        labels={
            "surface_covered_in_m2": "Surface Covered in m^2",
            "price": "Price",
            "property_type": "Property Type",
        },
    )
    ​
    fig.show()
    /tmp/ipykernel_659/3122900234.py:11: FutureWarning:
    
    In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
    
    
    Property Typeapartmenthousestore
    plotly-logomark
    xxxxxxxxxx
    Notice that the plot is interactive: you can rotate it zoom in or out. These kinds of plots also makes outliers easier to find; here, we can see that houses have higher prices than other types of properties.

    Notice that the plot is interactive: you can rotate it zoom in or out. These kinds of plots also makes outliers easier to find; here, we can see that houses have higher prices than other types of properties.

    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Modify the DataFrame to include columns for the base 10 log of price and surface_covered_in_m2 and then plot these for the entire mexico-city-real-estate-1.csv dataset.

    [5]:
     
    import math
    ​
    ​
    xxxxxxxxxx
    # Mapbox Scatter Plots

    Mapbox Scatter Plots¶

    xxxxxxxxxx
    A **mapbox scatter plot** is a special kind of scatter plot that allows you to create scatter plots in two dimensions and then superimpose them on top of a map. Our `mexico-city-real-estate-1.csv` dataset is a good place to start, because it includes **location data**. After importing the dataset and removing rows with missing data, split the `lat-lon` column into two separate columns: one for `latitude` and the other for `longitude`. Then use these to make a mapbox plot. Unfortunately, at present this type of plot does not easily allow for marker shape to vary based on a column of the DataFrame.

    A mapbox scatter plot is a special kind of scatter plot that allows you to create scatter plots in two dimensions and then superimpose them on top of a map. Our mexico-city-real-estate-1.csv dataset is a good place to start, because it includes location data. After importing the dataset and removing rows with missing data, split the lat-lon column into two separate columns: one for latitude and the other for longitude. Then use these to make a mapbox plot. Unfortunately, at present this type of plot does not easily allow for marker shape to vary based on a column of the DataFrame.

    [6]:
     
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1[["latitude", "longitude"]] = mexico_city1["lat-lon"].str.split(
        ",", 2, expand=True
    )
    mexico_city1["latitude"] = mexico_city1["latitude"].astype(float)
    mexico_city1["longitude"] = mexico_city1["longitude"].astype(float)
    fig = px.scatter_mapbox(
        mexico_city1,
        lat="latitude",
        lon="longitude",
        color="property_type",
        mapbox_style="carto-positron",
        labels={"property_type": "Property Type"},
        title="Distribution of Property Types for Sale in Mexico City",
    )
    fig.show()
    /tmp/ipykernel_659/3692783844.py:6: FutureWarning:
    
    In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
    
    
    © Carto © OpenStreetMap contributors
    Property TypeapartmenthousestoreDistribution of Property Types for Sale in Mexico City
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Create another column in the DataFrame with a log scale of the prices. Then create three separate plots, one for stores, another for houses, and a final one for apartments. Color the points in the plots by the log of the price.

    [7]:
     
    from math import log10
    ​
    ​
    xxxxxxxxxx
    # Choropleth Maps

    Choropleth Maps¶

    xxxxxxxxxx
    A Choropleth Map is a map composed of colored polygons, showing the variable of interest at different color depth across geographies.Plotly express has a function called `px.choropleth` that be used to plot Choropleth Map. The challenges here are getting the geometry information. There are two ways, one is to use the built-in geometries in plotly when plot US States (use the state name directly) and world countries (use ISP-3 code). Another way is to look for GeoJSON files where each location has geometry information. In the following example, we will show the plot in US States with a synthetic data set.  

    A Choropleth Map is a map composed of colored polygons, showing the variable of interest at different color depth across geographies.Plotly express has a function called px.choropleth that be used to plot Choropleth Map. The challenges here are getting the geometry information. There are two ways, one is to use the built-in geometries in plotly when plot US States (use the state name directly) and world countries (use ISP-3 code). Another way is to look for GeoJSON files where each location has geometry information. In the following example, we will show the plot in US States with a synthetic data set.

    [8]:
    xxxxxxxxxx
     
    # Create Synthetic Dataset
    df = pd.DataFrame.from_dict(
        {"State": ["CA", "TX", "NY", "HI", "DE"], "Temparature": [100, 120, 110, 90, 105]}
    )
    df
    [8]:
    State Temparature
    0 CA 100
    1 TX 120
    2 NY 110
    3 HI 90
    4 DE 105
    [9]:
    xxxxxxxxxx
     
    # Plot the data set in US map
    fig = px.choropleth(
        df, locations="State", locationmode="USA-states", color="Temparature", scope="usa"
    )
    fig.show()
    90100110120Temparature
    plotly-logomark
    xxxxxxxxxx
    # Histogram

    Histogram¶

    xxxxxxxxxx
    A **histogram** is a graph that shows the frequency distribution of numerical data. In addition to helping us understand frequency, histograms are also useful for detecting outliers. We can use the `px.histogram()` function from Plotly to draw histograms for specific columns, as long as the data type is numerical. Let's check the following example:

    A histogram is a graph that shows the frequency distribution of numerical data. In addition to helping us understand frequency, histograms are also useful for detecting outliers. We can use the px.histogram() function from Plotly to draw histograms for specific columns, as long as the data type is numerical. Let's check the following example:

    [10]:
    xxxxxxxxxx
     
    import plotly.express as px
    ​
    df = pd.read_csv("data/mexico-city-real-estate-1.csv")
    fig = px.histogram(df, x="price")
    fig.show()
    020M40M60M80M100M120M0200400600800
    pricecount
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Check the "surface_covered_in_m2" Histogram.

    [11]:
    xxxxxxxxxx
     
    fig = px.histogram(df,x="surface_covered_in_m2")
    fig.show()
    01000200030004000500060007000800002004006008001000
    surface_covered_in_m2count
    plotly-logomark
    xxxxxxxxxx
    # Boxplots

    Boxplots¶

    xxxxxxxxxx
    A **boxplot** is a graph that shows the minimum, first quartile, median, third quartile, and the maximum values in a dataset. Boxplots are useful because they provide a visual summary of the data, enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness. In the following example, we will explore how to draw boxplots for specific columns of a DataFrame.

    A boxplot is a graph that shows the minimum, first quartile, median, third quartile, and the maximum values in a dataset. Boxplots are useful because they provide a visual summary of the data, enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness. In the following example, we will explore how to draw boxplots for specific columns of a DataFrame.

    [12]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1.head()
    [12]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    Check the boxplot for column `"price"`:

    Check the boxplot for column "price":

    [13]:
    xxxxxxxxxx
     
    import plotly.express as px
    ​
    fig = px.box(mexico_city1, y="price")
    fig.show()
    020M40M60M
    price
    plotly-logomark
    xxxxxxxxxx
    If you want to check the distribution of a column value by different categories, defined by another categorical column, you can add an `x` argument to specify the name of the categorical column. In the following example, we check the price distribution across different property types:

    If you want to check the distribution of a column value by different categories, defined by another categorical column, you can add an x argument to specify the name of the categorical column. In the following example, we check the price distribution across different property types:

    [14]:
    xxxxxxxxxx
     
    fig = px.box(mexico_city1, x="property_type", y="price")
    fig.show()
    apartmenthousestore020M40M60M
    property_typeprice
    plotly-logomark
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Check the "surface_covered_in_m2" distribution by property types.

    [15]:
    xxxxxxxxxx
     
    fig = ...
    fig.show()
    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    Cell In [15], line 2
          1 fig = ...
    ----> 2 fig.show()
    
    AttributeError: 'ellipsis' object has no attribute 'show'
    xxxxxxxxxx
    # Bar Chart

    Bar Chart¶

    xxxxxxxxxx
    A **bar chart** is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale. 

    A bar chart is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

    In the following example, we will see some bar plots based on the Mexico City real estate dataset. Specifically, we will count the number of observations in each borough and plot them. We first need to read the data set and extract Borough and other location information from column "place_with_parent_names".

    [ ]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    # find location columns from place_with_parent_names
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1 = mexico_city1[mexico_city1["Borough"] != ""]
    ​
    mexico_city1.head()
    xxxxxxxxxx
    We can calculate the number of real estate showing in the data set by Borough using `value_counts()`, then plot it as bar plot:

    We can calculate the number of real estate showing in the data set by Borough using value_counts(), then plot it as bar plot:

    [ ]:
    xxxxxxxxxx
     
    # Use value_counts() to get the data
    mexico_city1["Borough"].value_counts()
    [ ]:
    xxxxxxxxxx
     
    # Plot value_counts() data
    fig = px.bar(mexico_city1["Borough"].value_counts())
    fig.show()
    xxxxxxxxxx
    We can plot more expressive bar plots by adding more arguments. For example, we can plot the number of observations by borough and property type. First of all, we need use `groupby` to calculate the aggregated counts for each Borough and property type combination:

    We can plot more expressive bar plots by adding more arguments. For example, we can plot the number of observations by borough and property type. First of all, we need use groupby to calculate the aggregated counts for each Borough and property type combination:

    [ ]:
    xxxxxxxxxx
     
    size_df = mexico_city1.groupby(["Borough", "property_type"], as_index=False).size()
    size_df.head()
    xxxxxxxxxx
    By specifying `x`, `y` and `color`, the following bar graph shows the total counts by Borough, with different property types showing in different colors. Note `y` has to be numerical, while `x` and `color` are usually categorical variables.<span style='color: transparent; font-size:1%'>WQU WorldQuant University Applied Data Science Lab QQQQ</span>

    By specifying x, y and color, the following bar graph shows the total counts by Borough, with different property types showing in different colors. Note y has to be numerical, while x and color are usually categorical variables.WQU WorldQuant University Applied Data Science Lab QQQQ

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="relative")
    fig.show()
    xxxxxxxxxx
    Note the argument `barmode` is specified as 'relative', which is also the default value. In this mode, bars are stacked above each other. We can also use 'overlay' where bars are drawn on top of each other.

    Note the argument barmode is specified as 'relative', which is also the default value. In this mode, bars are stacked above each other. We can also use 'overlay' where bars are drawn on top of each other.

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="overlay")
    fig.show()
    xxxxxxxxxx
    If we want bars to be placed beside each other, we can specify `barmode` as "group":

    If we want bars to be placed beside each other, we can specify barmode as "group":

    [ ]:
    xxxxxxxxxx
     
    fig = px.bar(size_df, x="Borough", y="size", color="property_type", barmode="group")
    fig.show()
    xxxxxxxxxx
    <font size="+1">Practice</font> 

    Practice

    Plot bar plot for the number of observations by property types in "mexico-city-real-estate-1.csv".

    [ ]:
    xxxxxxxxxx
     
    bar_df = ...
    ​
    fig = ...
    fig.show()
    xxxxxxxxxx
    # References and Further Reading

    References and Further Reading¶

    • Official plotly express Documentation on Scatter Plots
    • Official plotly Express Documentation on 3D Plots
    • Official plotly Documentation on Notebooks
    • plotly Community Forum Post on Axis Labeling
    • plotly express Official Documentation on Tile Maps
    • plotly Choropleth Maps in Python Document
    • plotly express Official Documentation on Figure Display
    • Online Tutorial on String Conversion in Pandas
    • Official Pandas Documentation on using Lambda Functions on a Column
    • Official Seaborn Documentation on Generating a Heatmap
    • Online Tutorial on Correlation Matrices in Pandas
    • Official Pandas Documentation on Correlation Matrices
    • Official Matplotlib Documentation on Colormaps
    • Official Pandas Documentation on Box Plots
    • Online Tutorial on Box Plots
    • Online Tutorial on Axes Labels in Seaborn and Matplotlib
    • Matplotlib Gallery Example of an Annotated Heatmap
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Visualizing Data: seaborn</strong></font>

    Visualizing Data: seaborn

    xxxxxxxxxx
    There are many ways to interact with data, and one of the most powerful modes of interaction is through **visualizations**. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: [pandas](../%40textbook/07-visualization-pandas.ipynb), [Matplotlib](../%40textbook/06-visualization-matplotlib.ipynb), [plotly express](../%40textbook/08-visualization-plotly.ipynb), and seaborn. In this section, we'll focus on using seaborn.

    There are many ways to interact with data, and one of the most powerful modes of interaction is through visualizations. Visualizations show data graphically, and are useful for exploring, analyzing, and presenting datasets. We use four libraries for making visualizations: pandas, Matplotlib, plotly express, and seaborn. In this section, we'll focus on using seaborn.

    xxxxxxxxxx
    # Scatter Plots

    Scatter Plots¶

    xxxxxxxxxx
    A **scatter plot** is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for **correlations**. 

    A scatter plot is a graph that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables, and are especially useful if you're looking for correlations.

    In the following example, we will see some scatter plots based on the Mexico City real estate data. Specifically, we can use scatter plot to show how "price" and "surface_covered_in_m2" are correlated. First we need to read the data set and do a little cleaning.

    [1]:
     
    import pandas as pd
    import seaborn as sns
    ​
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    ​
    mexico_city1.head()
    [1]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a...
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a...
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_...
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v...
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven...
    xxxxxxxxxx
    Use seaborn to plot the scatter plot for `"price"` and `"surface_covered_in_m2"`:

    Use seaborn to plot the scatter plot for "price" and "surface_covered_in_m2":

    [2]:
     
    sns.scatterplot(data=mexico_city1, x="price", y="surface_covered_in_m2");
    xxxxxxxxxx
    There is a very useful argument in `scatterplot` called `hue`. By specifying a categorical column as `hue`, seaborn can create a scatter plot between two variables in different categories with different colors. Let's check the following example using `"property_type"`:

    There is a very useful argument in scatterplot called hue. By specifying a categorical column as hue, seaborn can create a scatter plot between two variables in different categories with different colors. Let's check the following example using "property_type":

    [3]:
     
    sns.scatterplot(
        data=mexico_city1, x="price", y="surface_covered_in_m2", hue="property_type"
    );
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Plot a scatter plot for "price" and "surface_total_in_m2" by "property_type" for "mexico-city-real-estate-1.csv":

    [ ]:
     
    ​
    xxxxxxxxxx
    # Bar Charts

    Bar Charts¶

    xxxxxxxxxx
    A **bar chart** is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale. 

    A bar chart is a graph that shows all the values of a categorical variable in a dataset. They consist of an axis and a series of labeled horizontal or vertical bars. The bars depict frequencies of different values of a variable or simply the different values themselves. The numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

    In the following example, we will see some bar plots based on the Mexico City real estate dataset. Specifically, we will count the number of observations in each borough and plot them. We first need to import the dataset and extract the borough and other location information from column "place_with_parent_names".

    [4]:
    xxxxxxxxxx
     
    # Read Data
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    ​
    # Clean the data and drop `NaNs`
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    ​
    # find location columns from place_with_parent_names
    mexico_city1[
        ["First Empty", "Country", "City", "Borough", "Second Empty"]
    ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    mexico_city1 = mexico_city1.drop(["First Empty", "Second Empty"], axis=1)
    mexico_city1 = mexico_city1.dropna(axis=0)
    ​
    # Exclude some outliers
    mexico_city1 = mexico_city1[mexico_city1["price"] < 100000000]
    mexico_city1 = mexico_city1[mexico_city1["Borough"] != ""]
    ​
    mexico_city1.head()
    /tmp/ipykernel_721/836102575.py:12: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.
      ] = mexico_city1["place_with_parent_names"].str.split("|", 4, expand=True)
    
    [4]:
    operation property_type place_with_parent_names lat-lon price currency price_aprox_local_currency price_aprox_usd surface_total_in_m2 surface_covered_in_m2 price_per_m2 properati_url Country City Borough
    2 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 2700000.0 MXN 2748947.10 146154.51 61.0 61.0 44262.295082 http://cuauhtemoc.properati.com.mx/2pu_venta_a... México Distrito Federal Cuauhtémoc
    3 sell apartment |México|Distrito Federal|Cuauhtémoc| 19.41501,-99.175174 6347000.0 MXN 6462061.92 343571.36 176.0 128.0 49585.937500 http://cuauhtemoc.properati.com.mx/2pv_venta_a... México Distrito Federal Cuauhtémoc
    6 sell apartment |México|Distrito Federal|Miguel Hidalgo| 19.456564,-99.191724 670000.0 MXN 682146.11 36267.97 65.0 65.0 10307.692308 http://miguel-hidalgo-df.properati.com.mx/46h_... México Distrito Federal Miguel Hidalgo
    7 sell apartment |México|Distrito Federal|Gustavo A. Madero| 19.512787,-99.141393 1400000.0 MXN 1425379.97 75783.82 82.0 70.0 20000.000000 http://gustavo-a-madero.properati.com.mx/46p_v... México Distrito Federal Gustavo A. Madero
    8 sell house |México|Distrito Federal|Álvaro Obregón| 19.358776,-99.213557 6680000.0 MXN 6801098.67 361597.08 346.0 346.0 19306.358382 http://alvaro-obregon.properati.com.mx/46t_ven... México Distrito Federal Álvaro Obregón
    xxxxxxxxxx
    Let's check the example of a bar plot showing the value counts of each borough in the dataset. We first need to create a DataFrame showing the value counts:

    Let's check the example of a bar plot showing the value counts of each borough in the dataset. We first need to create a DataFrame showing the value counts:

    [5]:
     
    bar_df = pd.DataFrame(mexico_city1["Borough"].value_counts()).reset_index()
    bar_df
    [5]:
    index Borough
    0 Miguel Hidalgo 345
    1 Cuajimalpa de Morelos 255
    2 Álvaro Obregón 203
    3 Benito Juárez 198
    4 Tlalpan 171
    5 Iztapalapa 134
    6 Tláhuac 125
    7 Cuauhtémoc 120
    8 Gustavo A. Madero 89
    9 Venustiano Carranza 81
    10 Coyoacán 80
    11 La Magdalena Contreras 41
    12 Xochimilco 34
    13 Iztacalco 27
    14 Azcapotzalco 24
    15 Milpa Alta 1
    xxxxxxxxxx
    Since there are 16 different categories in Borough, we should increase the default plot size and rotate the x axis to make the plot more readable using the following syntax:

    Since there are 16 different categories in Borough, we should increase the default plot size and rotate the x axis to make the plot more readable using the following syntax:

    [6]:
     
    # Increase plot size
    sns.set(rc={"figure.figsize": (15, 4)})
    ​
    # Plot the bar plot
    ax = sns.barplot(data=bar_df, x="index", y="Borough")
    ​
    # Rotate the x axis
    ax.set_xticklabels(ax.get_xticklabels(), rotation=75)
    [6]:
    [Text(0, 0, 'Miguel Hidalgo'),
     Text(1, 0, 'Cuajimalpa de Morelos'),
     Text(2, 0, 'Álvaro Obregón'),
     Text(3, 0, 'Benito Juárez'),
     Text(4, 0, 'Tlalpan'),
     Text(5, 0, 'Iztapalapa'),
     Text(6, 0, 'Tláhuac'),
     Text(7, 0, 'Cuauhtémoc'),
     Text(8, 0, 'Gustavo A. Madero'),
     Text(9, 0, 'Venustiano Carranza'),
     Text(10, 0, 'Coyoacán'),
     Text(11, 0, 'La Magdalena Contreras'),
     Text(12, 0, 'Xochimilco'),
     Text(13, 0, 'Iztacalco'),
     Text(14, 0, 'Azcapotzalco'),
     Text(15, 0, 'Milpa Alta')]
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Plot a bar plot showing the value counts for property types in "mexico-city-real-estate-1.csv":

    [7]:
    xxxxxxxxxx
     
    pro_typ_df = pd.DataFrame(mexico_city1["property_type"].value_counts()).reset_index()
    pro_typ_df
    ​
    sns.barplot(data =pro_typ_df,x="index",y="property_type")
    ​
    [7]:
    <AxesSubplot:xlabel='index', ylabel='property_type'>
    xxxxxxxxxx
    # Correlation Heatmaps

    Correlation Heatmaps¶

    A correlation heatmap shows the relative strength of correlations between the variables in a dataset. Here's what the code looks like:

    [8]:
    xxxxxxxxxx
     
    import pandas as pd
    import seaborn as sns
    ​
    mexico_city1 = pd.read_csv("./data/mexico-city-real-estate-1.csv")
    mexico_city1 = mexico_city1.drop(
        ["floor", "price_usd_per_m2", "expenses", "rooms"], axis=1
    )
    mexico_city1 = mexico_city1.dropna(axis=0)
    mexico_city1_numeric = mexico_city1.select_dtypes(include="number")
    corr = mexico_city1_numeric.corr(method="kendall")
    sns.heatmap(corr)
    [8]:
    <AxesSubplot:>
    xxxxxxxxxx
    Notice that we dropped the columns and rows with missing entries before plotting the graph.

    Notice that we dropped the columns and rows with missing entries before plotting the graph.

    This heatmap is showing us what we might already have suspected: the price is moderately positively correlated with the size of the properties.

    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    The seaborn documentation on heat maps indicates how to add numeric labels to each cell and how to use a different colormap. Modify the plot to use the viridis colormap, have a linewidth of 0.5 between each cell and have numeric labels for each cell.

    [ ]:
    xxxxxxxxxx
     
    ​
    xxxxxxxxxx
    # References and Further Reading

    References and Further Reading¶

    • Official Plotly Express Documentation on Scatter Plots
    • Official Plotly Express Documentation on 3D Plots
    • Official Plotly Documentation on Notebooks
    • Plotly Community Forum Post on Axis Labeling
    • Plotly Express Official Documentation on Tile Maps
    • Plotly Express Official Documentation on Figure Display
    • Online Tutorial on String Conversion in Pandas
    • Official Pandas Documentation on using Lambda Functions on a Column
    • Official seaborn Documentation on Generating a Heatmap
    • Online Tutorial on Correlation Matrices in Pandas
    • Official Pandas Documentation on Correlation Matrices
    • Official Matplotlib Documentation on Colormaps
    • Official Pandas Documentation on Box Plots
    • Online Tutorial on Box Plots
    • Online Tutorial on Axes Labels in seaborn and Matplotlib
    • Matplotlib Gallery Example of an Annotated Heatmap
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited. WQU WorldQuant University Applied Data Science Lab QQQQ

    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    ​

    Usage Guidelines

    This lesson is part of the DS Lab core curriculum. For that reason, this notebook can only be used on your WQU virtual machine.

    This means:

    • ⓧ No downloading this notebook.
    • ⓧ No re-sharing of this notebook with friends or colleagues.
    • ⓧ No downloading the embedded videos in this notebook.
    • ⓧ No re-sharing embedded videos with friends or colleagues.
    • ⓧ No adding this notebook to public or private repositories.
    • ⓧ No uploading this notebook (or screenshots of it) to other websites, including websites for study resources.

    xxxxxxxxxx
    <font size="+3"><strong>Databases: SQL</strong></font>

    Databases: SQL

    [ ]:
     
    from IPython.display import YouTubeVideo
    xxxxxxxxxx
    # Working with SQL Databases

    Working with SQL Databases¶

    xxxxxxxxxx
    A database is a collection of interrelated data. The primary goal of a database is to store and retrieve information in a convenient and efficient way. There are many types of databases. In this section, we will be dealing with a **relational database**. A relational database is a widely used database model that consists of a collection of uniquely named **tables** used to store information. The structure of a database model with its tables, constraints, and relationships is called a **schema**. 

    A database is a collection of interrelated data. The primary goal of a database is to store and retrieve information in a convenient and efficient way. There are many types of databases. In this section, we will be dealing with a relational database. A relational database is a widely used database model that consists of a collection of uniquely named tables used to store information. The structure of a database model with its tables, constraints, and relationships is called a schema.

    A Structured Query Language (SQL), is used to retrieve information from a relational database. SQL is one of the most commonly used database languages. It allows data stored in a relational database to be queried, modified, and manipulated easily with basic commands. SQL powers database engines like MySQL, SQL Server, SQLite, and PostgreSQL. The examples and projects in this course will use SQLite.

    A table refers to a collection of rows and columns in a relational database. When reading data into a pandas DataFrame, an index can be defined, which acts as the label for every row in the DataFrame.

    xxxxxxxxxx
    # Connecting to a Database

    Connecting to a Database¶

    xxxxxxxxxx
    ## ipython-sql 

    ipython-sql¶

    xxxxxxxxxx
    ### Magic Commands

    Magic Commands¶

    xxxxxxxxxx
    Jupyter notebooks can run code that is not valid Python code but still affect the notebook . These special commands are called magic commands. Magic commands can have a range of properties. Some commonly used magic functions are below:

    Jupyter notebooks can run code that is not valid Python code but still affect the notebook . These special commands are called magic commands. Magic commands can have a range of properties. Some commonly used magic functions are below:

    Magic Command Description of Command
    %pwd Print the current working directory
    %cd Change the current working directory
    %ls List the contents of the current directory
    %history Show the history of the In [ ]: commands

    We will be leveraging magic commands to work with a SQLite database.

    xxxxxxxxxx
    ### ipython-sql

    ipython-sql¶

    xxxxxxxxxx
    `ipython-sql` allows you to write SQL code directly in a Jupyter Notebook. The `%sql` (or `%%sql`) magic command is added to the beginning of a code block and then SQL code can be written.

    ipython-sql allows you to write SQL code directly in a Jupyter Notebook. The %sql (or %%sql) magic command is added to the beginning of a code block and then SQL code can be written.

    xxxxxxxxxx
    ### Connecting with ipython-sql

    Connecting with ipython-sql¶

    xxxxxxxxxx
    We can connect to a database using the %sql magic function:

    We can connect to a database using the %sql magic function:

    [ ]:
     
    %load_ext sql
    %sql sqlite:////home/jovyan/nepal.sqlite
    xxxxxxxxxx
    ## sqlite3

    sqlite3¶

    xxxxxxxxxx
    We can also connect to the same database using the sqlite3 package:

    We can also connect to the same database using the sqlite3 package:

    [ ]:
     
    import sqlite3
    ​
    conn = sqlite3.connect("/home/jovyan/nepal.sqlite")
    xxxxxxxxxx
    # Querying a Database

    Querying a Database¶

    xxxxxxxxxx
    ## Building Blocks of the Basic Query

    Building Blocks of the Basic Query¶

    xxxxxxxxxx
    There are six common clauses used for querying data:

    There are six common clauses used for querying data:

    Clause Name Definition
    SELECT Determines which columns to include in the query's result
    FROM Identifies the table from which to query the data from
    WHERE filters data
    GROUP BY groups rows by common values in columns
    HAVING filters out unwanted groups from GROUP BY
    ORDER BY Orders the rows using one or more columns
    LIMIT Outputs the specified number of rows

    All clauses may be used together, but SELECT and FROM are the only required clauses. The format of clauses is in the example query below:

    SELECT column1, column2
    FROM table_name
    WHERE "conditions"
    GROUP BY "column-list"
    HAVING "conditions"
    ORDER BY "column-list"
    
    xxxxxxxxxx
    ## SELECT and FROM

    SELECT and FROM¶

    xxxxxxxxxx
    You can use `SELECT *` to select all columns in a table. `FROM` specifies the table in the database to query. `LIMIT 5` will select only the first five rows. 

    You can use SELECT * to select all columns in a table. FROM specifies the table in the database to query. LIMIT 5 will select only the first five rows.

    Example

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT *
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    You can also use `SELECT` to select certain columns in a table

    You can also use SELECT to select certain columns in a table

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT household_id,
           building_id
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use SELECT to select the district_id column from the id_map table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    We can also assign an **alias** or temporary name to a column using the `AS` command. Aliases can also be used on a table. See the example below, which assigns the alias `household_number` to `household_id`

    We can also assign an alias or temporary name to a column using the AS command. Aliases can also be used on a table. See the example below, which assigns the alias household_number to household_id

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT household_id AS household_number,
           building_id
    FROM id_map
    LIMIT 5
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use SELECT, FROM, AS, and LIMIT to select the first 5 rows from the id_map table. Rename the district_id column to district_number.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    ## Filtering and Sorting Data

    Filtering and Sorting Data¶

    xxxxxxxxxx
    SQL provides a variety of comparison operators that can be used with the WHERE clause to filter the data. 

    SQL provides a variety of comparison operators that can be used with the WHERE clause to filter the data.

    Comparison Operator Description
    = Equal
    > Greater than
    < Less than
    >= Greater than or equal to
    <= Less than or equal to
    <> or != Not equal to
    LIKE String comparison test
    xxxxxxxxxx
    For example, to select the first 5 homes in Ramechhap (district `2`):

    For example, to select the first 5 homes in Ramechhap (district 2):

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use WHERE to select the row with household_id equal to 13735001

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    ## Aggregating Data

    Aggregating Data¶

    xxxxxxxxxx
    Aggregation functions take a collection of values as inputs and return one value as the output. The table below gives the frequently used built-in aggregation functions:

    Aggregation functions take a collection of values as inputs and return one value as the output. The table below gives the frequently used built-in aggregation functions:

    Aggregation Function Definition
    MIN Return the minimum value
    MAX Return the largest value
    SUM Return the sum of values
    AVG Return the average of values
    COUNT Return the number of observations
    xxxxxxxxxx
    Use the `COUNT` function to find the number of observations in the `id_map` table that come from Ramechhap (district `2`):

    Use the COUNT function to find the number of observations in the id_map table that come from Ramechhap (district 2):

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT count(*)
    FROM id_map
    WHERE district_id = 2
    xxxxxxxxxx
    Aggregation functions are frequently used with a `GROUP BY` clause to perform the aggregation on groups of data. For example, the query below returns the count of observations in each District:

    Aggregation functions are frequently used with a GROUP BY clause to perform the aggregation on groups of data. For example, the query below returns the count of observations in each District:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT district_id,
           count(*)
    FROM id_map
    GROUP BY district_id
    xxxxxxxxxx
     `DISTINCT` is a keyword to select unique records in a query result. For example, if we want to know the unique values in the `district_id` column:

    DISTINCT is a keyword to select unique records in a query result. For example, if we want to know the unique values in the district_id column:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT distinct(district_id)
    FROM id_map
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use DISTINCT to count the number of unique values in the vdcmun_id column.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    `DISTINCT` and `COUNT` can be used in combination to count the number of distinct records. For example, if we want to know the number of unique values in the `district_id` column:

    DISTINCT and COUNT can be used in combination to count the number of distinct records. For example, if we want to know the number of unique values in the district_id column:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT count(distinct(district_id))
    FROM id_map
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use DISTINCT and COUNT to count the number of unique values in the vdcmun_id column.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    # Joining Tables

    Joining Tables¶

    xxxxxxxxxx
    Joins link data from two or more tables together by using a column that is common between the two tables. The basic syntax for a join is below, where `table1` and `table2` refer to the two tables being joined, `column1` and `column2` refer to columns to be returned from both tables, and `ID` refers to the common column in the two tables. 

    Joins link data from two or more tables together by using a column that is common between the two tables. The basic syntax for a join is below, where table1 and table2 refer to the two tables being joined, column1 and column2 refer to columns to be returned from both tables, and ID refers to the common column in the two tables.

    SELECT table1.column1,
           table2.column2
    FROM table_1
    JOIN table2 ON table1.id = table1.id
    
    xxxxxxxxxx
    We'll explore the concept of joins by first identifying a single household that we'd like to pull in building information for. For example, let's say we want to see the corresponding `foundation_type` for the first home in Ramechhap (District 1). We'll start by looking at this single record in the `id_map` table.

    We'll explore the concept of joins by first identifying a single household that we'd like to pull in building information for. For example, let's say we want to see the corresponding foundation_type for the first home in Ramechhap (District 1). We'll start by looking at this single record in the id_map table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT *
    FROM id_map
    WHERE district_id = 2
    LIMIT 1
    xxxxxxxxxx
    This household has `building_id` equal to 23. Let's look at the `foundation_type` for this building, by filtering the `building_structure` table to find this building.

    This household has building_id equal to 23. Let's look at the foundation_type for this building, by filtering the building_structure table to find this building.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT building_id,
           foundation_type
    FROM building_structure
    WHERE building_id = 23
    xxxxxxxxxx
    To join the two tables and limit the results to `building_id = 23`:    

    To join the two tables and limit the results to building_id = 23:

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    SELECT id_map.*,
           building_structure.foundation_type
    FROM id_map
    JOIN building_structure ON id_map.building_id = building_structure.building_id
    WHERE id_map.building_id = 23
    xxxxxxxxxx
    In addition to the basic `JOIN` clause, specific join types can be specified, which specify whether the common column needs to be in one, both, or either of the two tables being joined. The different join types are below. The left table is the table specified first, immediately after the `FROM` clause and the right table is the table specified after the `JOIN` clause. If the generic `JOIN` clause is used, then by default the `INNER JOIN` will be used.

    In addition to the basic JOIN clause, specific join types can be specified, which specify whether the common column needs to be in one, both, or either of the two tables being joined. The different join types are below. The left table is the table specified first, immediately after the FROM clause and the right table is the table specified after the JOIN clause. If the generic JOIN clause is used, then by default the INNER JOIN will be used.

    JOIN Type Definition
    INNER JOIN Returns rows where ID is in both tables
    LEFT JOIN Returns rows where ID is in the left table. Return NA for values in column, if ID is not in right table.
    RIGHT JOIN Returns rows where ID is in the right table. Return NA for values in column, if ID is not in left table.
    FULL JOIN Returns rows where ID is in either table. Return NA for values in column, if ID is not in either table.
    WQU WorldQuant University Applied Data Science Lab QQQQ
    xxxxxxxxxx
    The video below outlines the main types of joins:

    The video below outlines the main types of joins:

    [ ]:
    xxxxxxxxxx
     
    YouTubeVideo("2HVMiPPuPIM")
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use the DISTINCT command to create a column with all unique building IDs in the id_map table. LEFT JOIN this column with the roof_type column from the building_structure table, showing only buildings where district_id is 1 and limiting your results to the first five rows of the new table.

    [ ]:
    xxxxxxxxxx
     
    %%sql
    ​
    ​
    xxxxxxxxxx
    # Using pandas with SQL Databases

    Using pandas with SQL Databases¶

    xxxxxxxxxx
    To save the output of a query into a pandas DataFrame, we will use connect to the SQLite database using the SQLite3 package:

    To save the output of a query into a pandas DataFrame, we will use connect to the SQLite database using the SQLite3 package:

    [ ]:
    xxxxxxxxxx
     
    import sqlite3
    ​
    conn = sqlite3.connect("/home/jovyan/nepal.sqlite")
    xxxxxxxxxx
    To run a query using `sqlite3`, we need to store the query as a string. For example, the variable below called `query` is a string containing a query which returns the first 10 rows from the `id_map` table:

    To run a query using sqlite3, we need to store the query as a string. For example, the variable below called query is a string containing a query which returns the first 10 rows from the id_map table:

    [ ]:
    xxxxxxxxxx
     
    query = """
        SELECT *
        FROM id_map
        LIMIT 10
        """
    xxxxxxxxxx
    To save the results of the query into a pandas DataFrame, use the `pd.read_sql()` function. The optional parameter `index_col` can be used to set the index to a specific column from the query. 

    To save the results of the query into a pandas DataFrame, use the pd.read_sql() function. The optional parameter index_col can be used to set the index to a specific column from the query.

    [ ]:
    xxxxxxxxxx
     
    import pandas as pd
    ​
    df = pd.read_sql(query, conn, index_col="building_id")
    ​
    df.head()
    xxxxxxxxxx
    <font size="+1">Practice</font>

    Practice

    Try it yourself! Use the pd.read_sql function to save the results of a query to a DataFrame. The query should select first 20 rows from the id_map table.

    [ ]:
    xxxxxxxxxx
     
    query = ...
    ​
    df2 = ...
    ​
    df2.head()
    xxxxxxxxxx
    # References & Further Reading

    References & Further Reading¶

    • Additional Explanation of Magic Commands
    • ipython-SQL User Documentation
    • Data Carpentry Course on SQL in Python
    • SQL Course Material on GitHub (1)
    • SQL Course Material on GitHub (2)
    xxxxxxxxxx
    ---

    Copyright 2022 WorldQuant University. This content is licensed solely for personal use. Redistribution or publication of this material is strictly prohibited.

    xxxxxxxxxx
    Advanced Tools
    xxxxxxxxxx
    xxxxxxxxxx

    -

    Variables

    Callstack

      Breakpoints

      Source

      xxxxxxxxxx
      1
      10-databases-sql.ipynb
      • Working with SQL Databases
      • Connecting to a Database
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      • Querying a Database
      • Building Blocks of the Basic Query
      • SELECT and FROM
      • Filtering and Sorting Data
      • Aggregating Data
      • Joining Tables
      • Using pandas with SQL Databases
      • References & Further Reading
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